Video inspection systems, such as borescopes, have been widely used for capturing images or videos of difficult-to-reach locations by “snaking” image sensor(s) to these locations. Applications utilizing borescope inspections include aircraft engine blade inspection, power turbine blade inspection, internal inspection of mechanical devices, and the like.
A variety of techniques for inspecting the images or videos provided by borescopes for determining defects therein have been proposed in the past. Most such techniques capture and display images or videos to human inspectors for defect detection and interpretation. Human inspectors then decide whether any defect within those images or videos exists. When human inspectors look at many similar images of very similar blades of an engine stage, sometimes they miss defects because of the repetitive nature of the process or because of physical fatigue experienced by the inspector. Missing a critical defect may lead to customer dissatisfaction, transportation of an expensive engine back to service centers, lost revenue, or even engine failure.
Some other techniques utilize automated inspection techniques with many manually-set detection thresholds that are error-prone in an automated or semi-automated inspection system. In some of these other techniques, common defects are categorized into classes such as leading edge defects, erosion, nicks, cracks, or cuts and any incoming images or videos from the borescopes are examined to find those specific classes of defects. These techniques are thus focused on low-level feature extraction and identify damage by matching features and comparing to thresholds. Although somewhat effective, categorizing all kinds of blade damage defects within classes is difficult and images having defects other than those pre-defined classes are not detected.
Accordingly, it would be beneficial if an improved technique for performing borescope inspections were developed. It would additionally be beneficial if such a technique were automated, thus minimizing human intervention and the multiplicity of manually tuned thresholds.